Evaluation of Random Early Detection and Adaptive Random Early Detection in Benchmark Scenarios

  • Rohit P. Tahiliani School of Computer Science & Statistics, Trinity College Dublin, Ireland
  • Sagar Sachdeva School of Computer Science & Statistics, Trinity College Dublin, Ireland
  • Sachin Hadke School of Computer Science & Statistics, Trinity College Dublin, Ireland
  • Shane Sheehan School of Computer Science & Statistics, Trinity College Dublin, Ireland
  • Eamonn O' Nuallain School of Computer Science & Statistics, Trinity College Dublin, Ireland
Keywords: Active Queue Management, Random Early Detection, Bufferbloat

Abstract

In this paper, we evaluate Random Early Detection (RED) and Adaptive RED (ARED) in Benchmark Scenarios as detailed in RFC 7928. RED is one of the early proposed AQM mechanisms, which attains high throughput and keeps average delay low. Moreover, ARED is an extension to RED which eliminates the parameter sensitivity to improve the performance of RED. The results indicate that RED outperforms ARED in scenarios with abrupt changes in traffic load. ARED is known to reduce the packet drops and therefore, in rest of the scenarios it can be observed that ARED outperforms RED.

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Published
2018-03-22
How to Cite
Tahiliani, R. P., Sachdeva, S., Hadke, S., Sheehan, S., & Nuallain, E. O. (2018). Evaluation of Random Early Detection and Adaptive Random Early Detection in Benchmark Scenarios. Journal of Information Sciences and Computing Technologies, 7(1), 667-672. Retrieved from http://scitecresearch.com/journals/index.php/jisct/article/view/1408
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Articles